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CHAPTER 3: The Normal Distributions

CHAPTER 3: The Normal Distributions. ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation. Chapter 3 Concepts. Density Curves Normal Distributions The 68-95-99.7 Rule The Standard Normal Distribution Finding Normal Proportions

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CHAPTER 3: The Normal Distributions

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  1. CHAPTER 3:The Normal Distributions ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation

  2. Chapter 3 Concepts Density Curves Normal Distributions The 68-95-99.7 Rule The Standard Normal Distribution Finding Normal Proportions Using the Standard Normal Table Finding a Value When Given a Proportion

  3. Chapter 3 Objectives Define and describe density curves Measure position using percentiles Measure position using z-scores Describe Normal distributions Describe and apply the 68-95-99.7 Rule Describe the standard Normal distribution Perform Normal calculations

  4. Exploring a Distribution 4 In Chapters 1 and 2, we developed a kit of graphical and numerical tools for describing distributions. Now, we’ll add one more step to the strategy. Exploring Quantitative Data • Always plot your data: make a graph. • Look for the overall pattern (shape, center, and spread) and for striking departures such as outliers. • Calculate a numerical summary to briefly describe center and spread. • Sometimes the overall pattern of a large number of observations is so regular that we can describe it by a smooth curve.

  5. Density Curves Example: Here is a histogram of vocabulary scores of 947 seventh graders. The smooth curve drawn over the histogram is a mathematicalmodelfor the distribution.

  6. Density Curves The areas of the shaded bars in this histogram represent the proportion of scores in the observed data that are less than or equal to 6.0. This proportion is equal to 0.303. Now the area under the smooth curve to the left of 6.0 is shaded. If the scale is adjusted so the total area under the curve is exactly 1, then this curve is called a density curve. The proportion of the area to the left of 6.0 is now equal to 0.293.

  7. Density Curves • A density curveis a curve that: • is always on or above the horizontal axis • has an area of exactly 1 underneath it • A density curve describes the overall pattern of a distribution. The area under the curve and above any range of values on the horizontal axis is the proportion of all observations that fall in that range.

  8. Density Curves 8 • The median of a density curve is the equal-areas point, the point that divides the area under the curve in half. • The mean of a density curve is the balance point, at which the curve would balance if made of solid material. • The median and the mean are the same for a symmetric density curve. They both lie at the center of the curve. The mean of a skewed curve is pulled away from the median in the direction of the long tail. Our measures of center and spread apply to density curves as well as to actual sets of observations.

  9. Density Curves • The mean and standard deviation computed from actual observations (data) are denoted by and s,respectively. • The mean and standard deviation of the actual distribution represented by the density curve are denoted by µ (“mu”) and  (“sigma”),respectively.

  10. Normal Distributions • One particularly important class of density curves are the Normal curves, which describe Normal distributions. • All Normal curves are symmetric, single-peaked, and bell-shaped. • A Specific Normal curve is described by giving its mean µ and standard deviation σ.

  11. Normal Distributions • ANormal distributionis described by a Normal density curve. Any particular Normal distribution is completely specified by two numbers: its mean µ and standard deviation σ. • The mean of a Normal distribution is the center of the symmetric Normal curve. • The standard deviation is the distance from the center to the change-of-curvature points on either side. • We abbreviate the Normal distribution with mean µ and standard deviation σ as N(µ,σ).

  12. The 68-95-99.7 Rule • The 68-95-99.7 Rule • In the Normal distribution with mean µ and standard deviation σ: • Approximately 68% of the observations fall within σ of µ. • Approximately 95% of the observations fall within 2σ of µ. • Approximately 99.7% of the observations fall within 3σ of µ.

  13. Normal Distributions • The distribution of Iowa Test of Basic Skills (ITBS) vocabulary scores for 7th-grade students in Gary, Indiana, is close to Normal. Suppose the distribution is N(6.84, 1.55). • Sketch the Normal density curve for this distribution. • What percent of ITBS vocabulary scores are less than 3.74? • What percent of the scores are between 5.29 and 9.94?

  14. The Standard Normal Distribution • All Normal distributions are the same if we measure in units of size σ from the mean µ as center. The standard Normal distributionis the Normal distribution with mean 0 and standard deviation 1. If a variable x has any Normal distribution N(µ,σ) with mean µ and standard deviation σ, then the standardized variable has the standard Normal distribution, N(0,1).

  15. Finding Normal Proportions Areas under a Normal curve represent proportions of observations from that Normal distribution. There is no formula for areas under a Normal curve. Cumulative Proportions The cumulative proportion for a value x in a distribution is the proportion of observations in the distribution that are less than or equal to x.

  16. The Standard Normal Table Because all Normal distributions are the same when we standardize, we can find areas under any Normal curve from a single table. The Standard Normal Table Table A is a table of areas under the standard Normal curve. The table entry for each value z is the area under the curve to the left of z. Suppose we want to find the proportion of observations from the standard Normal distribution that are less than 0.81. We can use Table A: P(z < 0.81) = .7910

  17. Normal Calculations Find the proportion of observations from the standard Normal distribution that are between -1.25 and 0.81. Can you find the same proportion using a different approach? 1 – (0.1056+0.2090) = 1 – 0.3146 = 0.6854

  18. Normal Calculations How to Solve Problems Involving Normal Distributions • State: Express the problem in terms of the observed variable x. • Plan: Draw a picture of the distribution and shade the area of interest under the curve. • Solve: Perform calculations. • Standardizex to restate the problem in terms of a standard Normal variable z. • Use Table A and the fact that the total area under the curve is 1 to find the required area under the standard Normal curve. • Conclude: Write your conclusion in the context of the problem.

  19. .10 ? 70 Normal Calculations • How tall must a man be in the lower 10% for men aged 18 to 24? N(70, 2.8) • According to the Health and Nutrition Examination Study of 1976-1980, the heights (in inches) of adult men aged 18 to 24 are N(70, 2.8).

  20. .10 ? 70 Normal Calculations • How tall must a man be in the lower 10% for men aged 18 to 24? N(70, 2.8) Look up the closest probability (closest to 0.10) in the table. Find the corresponding standardized score. The value you seek is that many standard deviations from the mean. Z = -1.28

  21. .10 ? 70 Normal Calculations • How tall must a man be in the lower 10% for men aged 18 to 24? N(70, 2.8) Z = -1.28 We need to “unstandardize” the z-score to find the observed value (x): x = 70 + z(2.8) = 70 + [(-1.28 ) (2.8)] = 70 + (3.58) = 66.42 A man would have to be approximately 66.42 inches tall or less to place in the lower 10% of all men in the population.

  22. Chapter 3 Objectives Review Define and describe density curves Measure position using percentiles Measure position using z-scores Describe Normal distributions Describe and apply the 68-95-99.7 Rule Describe the standard Normal distribution Perform Normal calculations

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